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Record W4399140931 · doi:10.22148/001c.116915

A Counterfactual Canon

2024· article· en· W4399140931 on OpenAlexvenueno aff
Fedor Karmanov, Joshua Kotin

Bibliographic record

VenueJournal of Cultural Analytics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsnot available
Fundersnot available
KeywordsCounterfactual thinkingCanonPsychologyArtLiteratureSocial psychology

Abstract

fetched live from OpenAlex

The article analyzes the relationship between gender and taste at Shakespeare and Company. Using the *Shakespeare and Company Project* datasets, we discover that the majority of the books in the lending library were by men, and that women were almost twice as likely as men to borrow books by women. We also discover that the female authors with a high ratio of male to female readers are now canonical: Agatha Christie, Emily Dickinson, Gertrude Stein, Marianne Moore. In contrast, the female authors with the highest ratio of female to male readers are less well-known: Margaret Kennedy, E. M. Delafield, Rebecca West, Elizabeth von Arnim. These final two discoveries are surprising: they suggest that the reading practices of men determined the canon of female authors, and that the reading practices of women might reveal a counterfactual canon of modernism. We consider how this counterfactual canon of female authors might influence future work in literary history.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.060
metaresearch head score (Gemma)0.240
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.240
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0030.008
Scholarly communication0.0070.008
Open science0.0030.006
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0260.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.341
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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